This invention relates to user-guided searching. More particularly, this invention relates to user-guided searching of a plurality of objects wherein a user may indicate user-preference for a subset of the objects from a large collection to efficiently locate, for the benefit of the user, the most preferred objects from the large collection.
Conventional database searching, including the Internet, is typically performed by querying a particular database. Essentially, a searcher enters some broad search parameters, such as particular keywords, into a database search engine. The searcher then examines and evaluates the results. One typical way in which searchers attempt to get “better” search results is to modify the query and perform another search with the “better” query. Eventually, the searcher typically stops when the searcher has found what is believed to be relevant. This searching process (modifying queries based on past search results) can be a tedious process. Further, such a process does not provably result in searcher-preferred results.
No system exists that gives a searcher the capability to indicate that objects, or alternatives, in a collection are “likable” or “not likable”, wherein the searcher's preferences are then ranked according to an approximation of the searcher's value function. Stated another way, no system exists that works to approximate the user's/searcher's value function with enough feedback to optimally rank order objects in a collection. Further, no system exists that gives a searcher the capability to directly compare and refine prior comparisons of objects, such objects defined by discrete attributes, to one another. Even further, no such systems exist that gather searcher feedback at the object level. Still further, no such systems exist that learn a searcher's preferences as the searcher examines and evaluates search results.
Therefore, a need exists for such a system that gives a searcher the capability to indicate that objects, or alternatives, in a collection are “likable” or “not likable”, wherein the searcher's preferences are then ranked according to an approximation of the searcher's value function. And, a need exists for a system that gives a searcher the capability to directly compare and refine prior comparisons of objects, such objects defined by discrete attributes, to one another. Also, a need exists for a system that gathers searcher feedback at the object level. And, a need exists for a system that learns a searcher's preferences as the searcher examines and evaluates search results